An intelligent quick prediction algorithm with applications in industrial control and loading problems

Yong Yu, Tsan Ming Choi, Chi Leung Hui

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)


The Artificial Neural Network (ANN) and its variations have been well-studied for their applications in the prediction of industrial control and loading problems. Despite showing satisfactory performance in terms of accuracy, the ANN models are notorious for being slow compared to, e.g., the traditional statistical models. This substantially hinders ANN model's real-world applications in control and loading prediction problems. Recently a novel learning approach of ANN called Extreme Learning Machine (ELM) has emerged and it is proven to be very fast compared with the traditional ANN. In this paper, an Intelligent Quick Prediction Algorithm (IQPA), which employs an extended ELM (ELME) in producing fast, stable, and accurate prediction results for control and loading problems, is devised. This algorithm is versatile in which it can be used for short, medium to long-term predictions with both time series and non-time series data. Publicly available power plant operations and aircraft control data are employed for conducting analysis with this proposed novel model. Experimental results show that IQPA is effective and efficient, and can finish the prediction task with accurate results within a prespecified time limit.
Original languageEnglish
Article number6080740
Pages (from-to)276-287
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Issue number2
Publication statusPublished - 1 Apr 2012


  • Hybrid model
  • quick intelligent prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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